Datacast Episode 16: Bayesian Probabilistic Programming with Peadar Coyle

Datacast’s 16th episode is my chat with Peadar Coyle, a data scientist and entrepreneur based in London. Give it a listen to learn about the importance of providing end-to-end value as a data scientist, the rising popularity of probabilistic programming, why data scientists should understand the soft side of technical decision making and care about ethics, and much more.

Peadar Coyle is a Data Scientist and Engineer based in London. He regularly speaks at conferences and has written a book consisting of numerous interviews with Data Scientists throughout the world. He is also a passionate Open-Source evangelist, himself a supporter who has contributed to PyMC3. Most recently, he founded a stealth startup that is working on hyper-personalized audio.


Show Notes

  • (2:02) Peadar discussed his undergraduate experience studying Physics and Philosophy at the University of Bristol.

  • (3:05) Peadar then pursued a Master’s degree in Mathematics from the University of Luxembourg, where he did a thesis on machine learning for time series forecasting.

  • (4:16) Peadar commented on his varied work experience with various companies, particularly on data maturity and the difference between established companies and startups.

  • (7:11) Peadar talked about his latest startup called aflorithmic Labs, which develops tech platform that powers and enables the creation of a new generation hyper-personalized / super-relevant podcasts.

  • (8:13) In the series “Interviews with Data Scientists,” Peadar interviewed with 24 of the world’s most influential and innovative data scientists from across the spectrum. He talked about the common traits in the best data scientists.

  • (10:05) Peadar mentioned his contribution to PyMC3, a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms.

  • (11:32) Peadar talked about the probabilistic programming survey he conducted recently, in which A/B testing is a big use case.

  • (13:37) In his talk “Lies damned lies and statistics in Python” at PyData London 2016, Peadar compared and debugged models in Statsmodels, scikit-learn and PyMC3. He recalled the differences here.

  • (15:27) Peadar went over “Probabilistic Programming Primer” — an online course he designed to teach people to learn how to enhance modeling abilities and better communicate risk.

  • (18:32) Peadar talked about the recent development in the PyData ecosystem, in reference to his talk “A Map of the PyData Stack” at PyData Amsterdam 2016.

  • (20:18) Discussing his blog post “How to successfully deliver Data Science in the Enterprise,” Peadar went over the people, processes, and things that are required to make data science a successful component in enterprise businesses.

  • (23:25) Discussing his blog post “Building Full-Stack Vertical Data Products,” Peadar emphasized the importance of providing end-to-end value with lean metrics as a data scientist.

  • (29:50) Discussing his blog post “One weird tips to improve the success of DS projects,” Peadar shared his small practice of writing down the risks before embarking on a project.

  • (32:58) Discussing his blog post “3 pitfalls for non-technical managers managing DS teams,” Peadar described the things that non-technical managers will get wrong in managing a technical project.

  • (35:31) Discussing his blog post “What does it mean to be a Senior DS?,” Peadar explained why senior data scientists should understand the soft side of technical decision making and should care about ethics.

  • (38:57) Peadar gave a brief overview of machine learning interpretability.

  • (40:21) Closing segments.

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